Automated Organization ProfileInstitute of Urban Environment
Institute of Urban Environment
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets in this organization
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the organization's datasets
Total Mentions
Total mentions of the organization's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 65.4 (sum of 50 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This database presents a 13-year dataset (2010–2022) from two adjacent subtropical reservoirs (Shidou and Bantou) in Xiamen, Fujian Province, Southeast China. It provides a quarterly overview of key statistical characteristics of the time series, including physicochemical parameters, microscope-based phytoplankton, and DNA sequence-based bacteria and microeukaryotes.
Authors
- Shuzhen Li ;
- Huihuang Chen ;
- Jin, Lei ;
- Xiao, Peng ;
- Yang, Jun R ;
- Zjie Xu ;
- Lemian Liu ;
- Yang, Jun
The dataset is the raw data and experimental results of manuscript "Scale effects of the spatiotemporal relationship between nighttime light and population activity intensity: A case study in Shanghai, China".
Authors
- Guo, Xiangzhong
The dataset is the raw data and experimental results of manuscript "Scale effects of the spatiotemporal relationship between nighttime light and population activity intensity: A case study in Shanghai, China".
Authors
- Guo, Xiangzhong
To explore the impacts of human disturbance and urbanization on airborne microbiome and virome, settled dust (B)and dust samples (A)were respectively collected from the rural, suburban, and urban areas of Xiamen (24°26′46″N 118°04′04″E), Fujian province of China. The settled dust was collected from the least-handled surfaces, which are minimum 1.5 m above floor level. Meanwhile, the dust sample was collected from the impervious surface nearby the settled dust sampling site. About 50 g of settled dust or dust were collected in each site and filtered through a 10-mesh sieve to discard stones and debris. The microbial and viral DNA were extracted from the sieved duts and settled dust, and sequencing on a Novaseq 6000 platform.
Authors
- Li, Hu ;
- Jianqiang, Su
Agrivoltaics is a farming method that strategically integrates solar panels with agricultural production, a dual-use system that boosts food production while generating clean energy. China is the one of leading countries in agrivoltaics. However, no robust vectorized dataset has been available to verify the distribution of agrivoltaics in China. This study aims to provide the first nationwide agrivoltaics distribution and type dataset in China using comprehensive identification methods based on published spatial data of photovoltaic power stations and agrivoltaics records. The overall accuracy of agrivoltaics through visual examination is 89.71%. The results show that: (1) By 2022, there are 1,678 agrivoltaics projects in China with a total installation capacity of 134.55 GW. (2) China launched its first commercial agrivoltaics in 2010, reaching a peak of 347 projects in 2017, after which the number of new agrivoltaics projects has remained no less than 140 annually. (3) The three most common agrivoltaics types are crop-based, fishery-based, and greenhouse-based. This vectorized agrivoltaics dataset will support macro-level management and the sustainable development of agrivoltaics.
Authors
- Xueyan Zhang ;
- Ma, Xin
16S rRNA and ITS sequencing raw data of indoor dust
Authors
- Lu, Long ;
- Qiansheng, Huang
The samples were collected at a long-term monitoring station managed by the Fuyang Agricultural Bureau in Zhejiang Province, China. Soil (samples S1-S9) and earthworm (samples E1-E9) samples were collected from the surface (5-15cm). The numbers 1-3 in the sample name indicate no fertilizer control; The numbers 4-6 represent chemical nitrogen (N), phosphorus (P), and potassium (K) fertilizer treatments; The numbers 7-9 represent treatments that combine chemical fertilizers (NPK) with commercial organic fertilizers In addition, due to insufficient DNA concentration, samples E1 and E5 were excluded from sequencing; Samples E7, E8, and E9 were merged into a library named E789.
Authors
- cui hong xia ;
- Hu, Liao ;
- Jianqiang, Su
glacial foreland metagenomes 202109
Authors
- Liao, Hu ;
- Su, Jian-Qiang
Biodegradation is a sustainable strategy to address global microplastics (MPs) pollution but is constrained by the lack of efficient degrading microbes and effective tools to harness them. Here, we developed a function-driven single-cell approach to precisely identify and recover MPs-degrading microorganisms from complex microbiota by integrating isotope-labeled single-cell Raman spectroscopy with targeted cell sorting, sequencing and culturing. Using heavy water and MPs as the sole carbon source, Raman spectroscopy effectively identified active microbes capable of degrading multiple types of MPs from insect gut microbiota. Raman-guided single-cell sorting and sequencing revealed seven previously unrecognized polystyrene degraders and mapped key enzymes involved in each degradation stage. Furthermore, live-cell Raman-guided sorting enabled the cultivation of rare but highly active polystyrene degraders often missed by conventional methods. This “screen-first, culture-second” single-cell approach offers a powerful and scalable platform to accelerate MPs biodegradation and supports the development of microbial solutions to mitigate global plastic pollution.
Authors
- Guo, Hong-Qin ;
- Yang, Kai ;
- Xing, Xin-Yu ;
- Yang, Yu-Nan ;
- Long-Ji Zhu ;
- Kang, Xiao-Xi ;
- Ju, Feng ;
- Ji, Rong ;
- Corvini, Philippe Francois-Xavier ;
- Yong-Guan Zhu ;
- Cui, Li
Understanding ecological and evolutionary mechanisms that drive biodiversity patterns is important for comprehending biodiversity. Despite being critically important to the functioning of ecosystems, the mechanisms driving belowground biodiversity are little understood. We here investigated the radiation and trait diversity of soil oribatid mites from two mountain ranges, i.e. the Alps in Austria and Changbai Mountain in China, at similar latitude in the temperate zone differing in formation processes (orogenesis) and exposed to different climates. We collected and sequenced soil oribatid mites from forests at 950 to 1700 m at each mountain and embedded them into the chronogram of species from temperate Eurasia. We investigated the phylogenetic age of oribatid mites and compared the node age of species with the mountain uplift time of the Alps and Changbai Mountain. We then inspected trophic variation, geographical range size and reproductive mode, and identified traits that promote oribatid mite survival and evolution in montane forest ecosystems. We found that oribatid mites on Changbai Mountain are phylogenetically older than species in the Alps. All species on Changbai Mountain evolved long before the uplift of Changbai Mountain, but some species in the Alps evolved after the orogenesis of the Alps. On Changbai Mountain more species possess broader trophic variation, have larger geographical range sizes and more often reproduce via parthenogenesis compared to species from the Alps. Species on Changbai Mountain survived the mountain uplift or colonized the mountain thereafter supporting the view that generalistic traits promote survival and evolution in phylogenetically old soil animal species. Collectively, our findings highlight that combining species traits and phylogeny allow deeper insight into the evolutionary forces shaping soil biodiversity in montane ecosystems.
Authors
- Pan, Xue ;
- Heimburger, Bastian ;
- Chen, Ting-Wen ;
- Lu, Jing-Zhong ;
- Cordes, Peter Hans ;
- Xie, Zhijing ;
- Sun, Xin ;
- Liu, Dong ;
- Wu, Donghui ;
- Scheu, Stefan ;
- Schaefer, Ina ;
- Maraun, Mark